The human body reacts constantly to changes in its environment. Microbial invasions induce immunological responses [1]; social threats trigger the neuroendocrine cascade of the stress response [2] and can alter immune stance [3]; toxin, toxicant, and pollutant exposures elicit protective responses across multiple systems [4] and can induce adverse effects [5]; and change in ambient temperature can disrupt homeostasis [6]. On a cellular level, actions are taken every second based on signals received from the environment. Several factors can influence the effect of these exposures and the body’s response, importantly, the levels of certain nutrients, and the overall nutritional status [7]. Environmental signals driving changes in our biology are constant while being variable in space and time [8]. Traditionally, scientists have studied the influence of the environment on human health using a reductionist approach, exploring the effect of a single environmental exposure on a health outcome. However, the recent emergence of the exposome concept provides an alternative framework to holistically study the environment. The exposome encompasses all exposures, throughout the life course [9], and includes internal processes, like endogenous metabolism and microbial-derived metabolites, specific external exposure, like exposure to toxic environmental chemicals and dietary nutrients, and wider social factors that can influence an individual, like financial status and education [10]. It represents the environmental complement to the genome, that is, a comprehensive characterization of the molecular exposures to which an organism is subject [11-13]. For the purpose of this review, we define the exposome as all exposures, including lived experiences, that can be assessed throughout the life course, but recognize the need to focus on those exposures that can be measured with current technologies. We focus primarily on toxic exposures but we acknowledge other important factors that can influence the exposome or the body’s response to exposure, such as: (i) nutritional status [7] and the microbiome [14] and (ii) other factors of the external exposome such as education [15] and family environment [16]. While there have been some attempts at characterizing the biological footprint of these factors [17], they remain difficult to measure. Newer studies provide hope and directions on how these factors may be tested in the future [18].

The many chronic diseases of aging are thought to derive from common underlying processes that precipitate molecular changes over time [19]. These common mechanisms have been classified into nine hallmarks of aging: genomic instability, epigenetic alterations, telomere attrition, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication [20]. Aging is influenced by genetic factors [21]; however, current estimates are that <10% of differences in longevity between individuals can be attributed to inherited genes [22]. Environmental factors are therefore critical determinants of aging processes [23].

The human body can activate compensatory pathways that provide a form of resilience against damaging factors that accumulate with age. It is proposed that individual differences in this process of resilience determine whether a life course would end relatively healthy, frail, or in early mortality [24, 25]. Adverse environmental exposures can induce damage and impose additional stress on resilience processes, reducing the reserve needed to counter the effect of biological aging, thus, increasing the risk of poor or accelerated aging.

Time is an important variable that can influence the effect of environmental exposures. Both the timing of exposure during a life course as well as the duration of an exposure can determine the physiological response. It is important to identify critical time windows of exposure across the life course that might influence an outcome of interest since the scale of the response to an exposure would depend on whether the exposure occurred in utero, during development, in mid-life, or later in life [26, 27]. Damaging environmental exposures may be for a short period of time (an acute exposure) or over a prolonged period (a chronic exposure). The response to an acute or transient exposure may subside once the exposure has been removed from the system through metabolism and excretion but may leave signs of lasting damage that can be detected through molecular probes, like epigenetic marks of exposure to cigarette smoke in former smokers [28] or epigenetic changes in immune cells in response to pathogenic exposure [29] whereas response to a chronic exposure is usually maintained to counter the effects of long-term exposure. Thus, exposures may induce temporary or permanent changes in molecular pathways. Both aspects of time can influence whether damage is caused and the organism’s response to the exposure.

Several studies have reported associations between environmental exposures and different hallmarks of aging. For example, exposure to air pollution [30], pesticides [31], heavy metals [32], industrial solvents [33], and viruses and bacteria [34] has been associated with mitochondrial dysfunction but no study has investigated the effect of their concomitant exposure on mitochondrial function. Different environmental exposures are correlated, for example, people who live in polluted places tend to lead stressful lives and have reduced access to healthy nutrition, exercise, and leisure. For this reason, studying any one exposure may (a) confuse the apparent effects of the specific exposure with some other, correlated exposures and (b) fail to capture the myriad of exposures that represent the actual environmental burden. In order to illustrate the contributions of various exposures to hallmarks of aging, we have organized observed associations between exposures and the hallmarks of aging (Figure 1). This was created by first categorizing exposures into nine classes: air pollution, pesticides, metals/metalloids, mutagens, industrial solvents, plasticizers and related compounds, ambient temperature, microbes, and cigarette smoke. Each of these categories can comprise of multiple exposures and are grouped due to similarity in source, or chemical structure or properties. In order to confirm an association between the exposure class and a hallmark of aging, we ran multiple searches in PubMed using a combination of the name of each chemical category and, “aging,” “hallmarks of aging,” or “toxicity.” The figure illustrates that different environmental exposures affect many different hallmarks of aging; therefore, an exposomic framework is needed to study environmental drivers of aging. Additionally, the influence of the exposome on the aging process would be modified by the underlying nutritional status and needs to be studied under these contexts [7, 35]. To the best of our knowledge, studies of aging have rarely adopted an exposomic framework.

Compiling exposomic data requires integrating information from multiple sources. Broadly, the sources of data can be geospatial in origin or from personal monitoring [11, 36, 37].

“Geospatial data” can be used to infer individual-level exposure from ecological data. Using data describing the spatial distribution of a toxicant, an estimate of an individual’s exposure to that toxicant can be made using their residential address. These data have been used to estimate exposure to air pollution, pesticides [38], access to green space, proximity to pollution sources, proximity to a contaminated water source, and proximity to landfills [39–41]. Data needed to measure the spatial distribution of a toxicant come from:

“Personal monitoring data” provide individual-level exposure assessment that can be made through several methods:

These data sources enable measuring exposures on a national, regional, or individual level, and at specific time points in a life course. In this paper, we provide an overview of how an exposomic framework could be applied to studies of environmental drivers of aging. We also illustrate the utility of model organisms to follow up observations made in epidemiological settings.

While a randomized clinical trial (RCT) is the gold standard design to uncover causal associations, it is unethical to conduct RCTs of exposure to many environmental toxicants. Instead, the scientific community relies on quasi-experimental observational studies. In order to apply an exposomic framework, study designs that are disease-agnostic and focused on generating a representative sample of the population of interest to enable hypothesis-free analysis of a broad range of exposure will be critical. There are several ways this could be achieved. One possibility is to create birth cohorts that are prospectively observed through their life course [59] with the following considerations:

Birth cohorts are expensive, resource intensive, and prone to attrition. A complementary approach to investigate the exposome in an aging context can leverage existing infrastructure and data. Indeed, population-based cohorts with representation of different age windows are critical for an unbiased exploration of the exposome. Over decades, several cohort studies have been established that can provide the necessary data for this (Table 1). By creatively using existing data, for example, leveraging natural experiments created through changes in policies [62, 63], we can allocate resources toward the infrastructure needed to characterize the exposome, such as creating exposomic profiles of biological samples available in existing cohorts [64, 65]. Apart from integrating exposure estimates to cohort studies, it will be also be beneficial to consider ways to integrate data across cohorts representing different time points in the life course. This could be achieved by conducting parallel analyses that draw samples from cohorts at the different life course stages, like those conducted under the HELIX project in Europe [66] and in a study that leveraged data from people exposed to Arsenic in Chile and Bangladesh [67].

When using existing resources, some considerations include:

Many aspects of biological aging were first discovered in model organisms Caenorhabditis elegans and Drosophila melanogaster. Model organisms remain an important tool in uncovering mechanisms of biological aging and to discover interventions. These models can also play a role in understanding biological plausibility and gradient, which are part of Hill’s causal criteria [108], of any significant findings in observational settings.

The long history of C. elegans in genetic and neuroscience research provides a rich resource in the pursuit of environmental drivers of aging. The large number of existing mutant libraries provides limitless opportunity and their genetic tractability makes them useful models to find as yet uncharacterized relationships between exposures and genetic susceptibility. Caenorhabditis elegans are amenable to high-throughput screens and have been used by the National Toxicology Program for this purpose [109]. Studies have found good correspondence between LD₅₀ values of several toxicants in rodents and C. elegans [110]. An example of the type of high-throughput approaches is the COPAS biosorter, which is a flow cytometer that can handle particles as large as C. elegans. The instrument has been used to screen for reproductive and neurodegenerative toxicants [111–113].

Over decades, researchers have developed several open source methods that favor the use of C. elegans in aging research. For example, the lifespan machine developed by Stroustrup et al. [114] makes it possible to measure the lifespan of thousands of worms with significantly reduced human involvement. As another example, several researchers have developed methods to study associative memory in the organism [115]. Additionally, researchers are able to characterize tissue-specific gene expression [116], miRNA expression [117], histone modifications [118], and global metabolism [111, 119]. They are also excellent models to study reproductive aging and development [113, 120].

Data from whole organisms can be complimented by predictive toxicology, which may provide evidence of exposure, metabolism, absorption, and potential toxic effects of exposure using computational methods. The adverse outcome pathway (AOP) framework has been used to improve mechanistic understanding and to predict adverse effects of exposure using existing toxicological evidence [121]. While the use of AOP framework has generally been limited to individual chemicals, experts have recommended its use in exposome research by moving from linear pathways to networks of pathways, thus considering multiple causes for adverse effects [122]. The use of AOPs in the exposome context can help create a priori mechanistic links between exposure to mixtures and adverse outcomes relevant to the aging process [123].

The molecular hallmarks of aging [20], established from studies of cells and model organisms, are difficult to measure in studies of humans. As a result, there remains no gold standard measure of human aging, although a range of demographic, clinical, and molecular methods have been proposed [24, 124]. In demography, researchers quantify aging at the population level from differences in mortality rates across chronological ages [125]; in clinical gerontology, researchers quantify aging at the patient level from accumulations of chronic diseases and functional deficits [126] and measures of physical frailty [127]; in the emerging field of geroscience, which seeks to translate basic science in the biology of aging to prevent chronic disease [19, 128], researchers quantify aging using algorithms that summarize omics data to estimate the state or pace of decline in system integrity, referred to as biological aging [129–131]. None of these outcomes has yet received substantial research attention in an exposomic framework. However, early studies investigating impacts of environmental pollutants suggest substantial promise [132–135].

Similar to the exposome, these processes change over time, that is, they evolve over decades. This creates the challenge of intersecting dynamic exposures to dynamic biological processes, especially since we are interested in understanding when biological aging deviates from chronological aging.

With a rapidly aging global population, understanding the drivers of aging is more important than ever before. The exposome framework is responsive to the reality of human exposures, they are variable in space and time, and occur concomitantly. In the paper, we have described some ways in which the exposome framework could be applied to aging research; however, challenges remain in capturing profiles of chemicals with: short half-lives, low abundance in the body, and those present in a minor subset of the population. Despite these limitations, using an exposomic framework can provide a realistic assessment of major environmental drivers of aging, providing a means to prioritize policies and interventions that can prevent unhealthy aging. Birth cohorts should be set up in several locations worldwide to ensure representation of low- and middle-income countries. International projects like the 1000 genomes project or the HAPMAP project may provide a valuable template to create exposomics profiles in populations across the globe [136, 137]. The availability of high-quality assessment of molecular hallmarks of aging is critical to studying environmental drivers of aging and resources must be directed at improving readouts of hallmarks of aging. Integration of historical environmental exposure data with existing population studies has the potential to accelerate this research agenda.

This work was funded by National Institutes of Health grants P30 ES009089 and UL1TR001873.

Dr. Miller receives royalties from his books The Exposome: A Primer and The Exposome: a New Paradigm for the Environment and Health. He also receives an annual stipend for his service as Editor-in-Chief of Exposome.